Visible and NIR Image Fusion Algorithm Based on Information Complementarity

TL;DR

This paper introduces a novel visible and NIR image fusion algorithm that leverages physical signal properties and information complementarity to enhance image quality while avoiding color distortion, outperforming existing methods.

Contribution

The paper proposes a new fusion model based on physical signal levels and a multi-layer filtering approach to better utilize spectrum properties and information complementarity.

Findings

Effective preservation of spectrum properties and complementarity.

Reduced color distortion and artifacts.

Outperforms state-of-the-art fusion algorithms.

Abstract

Visible and near-infrared(NIR) band sensors provide images that capture complementary spectral radiations from a scene. And the fusion of the visible and NIR image aims at utilizing their spectrum properties to enhance image quality. However, currently visible and NIR fusion algorithms cannot well take advantage of spectrum properties, as well as lack information complementarity, which results in color distortion and artifacts. Therefore, this paper designs a complementary fusion model from the level of physical signals. First, in order to distinguish between noise and useful information, we use two layers of the weight-guided filter and guided filter to obtain texture and edge layers, respectively. Second, to generate the initial visible-NIR complementarity weight map, the difference maps of visible and NIR are filtered by the extend-DoG filter. After that, the significant region of NIR night-time compensation guides the initial complementarity weight map by the arctanI function. Finally, the fusion images can be generated by the complementarity weight maps of visible and NIR images, respectively. The experimental results demonstrate that the proposed algorithm can not only well take advantage of the spectrum properties and the information complementarity, but also avoid color unnatural while maintaining naturalness, which outperforms the state-of-the-art.